145 related articles for article (PubMed ID: 37097394)
1. The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance.
Wang Y; Luo F; Yang X; Wang Q; Sun Y; Tian S; Feng P; Huang P; Xiao H
J Cancer Res Clin Oncol; 2023 Sep; 149(11):8581-8592. PubMed ID: 37097394
[TBL] [Abstract][Full Text] [Related]
2. A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.
Li L; Mei Z; Li Y; Yu Y; Liu M
Comput Biol Med; 2024 Jun; 175():108519. PubMed ID: 38688128
[TBL] [Abstract][Full Text] [Related]
3. MIST: multiple instance learning network based on Swin Transformer for whole slide image classification of colorectal adenomas.
Cai H; Feng X; Yin R; Zhao Y; Guo L; Fan X; Liao J
J Pathol; 2023 Feb; 259(2):125-135. PubMed ID: 36318158
[TBL] [Abstract][Full Text] [Related]
4. Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet.
Peng L; Wang C; Tian G; Liu G; Li G; Lu Y; Yang J; Chen M; Li Z
Front Microbiol; 2022; 13():995323. PubMed ID: 36212877
[TBL] [Abstract][Full Text] [Related]
5. Transformaer-based model for lung adenocarcinoma subtypes.
Du F; Zhou H; Niu Y; Han Z; Sui X
Med Phys; 2024 Mar; ():. PubMed ID: 38427790
[TBL] [Abstract][Full Text] [Related]
6. One-step algorithm for fast-track localization and multi-category classification of histological subtypes in lung cancer.
Qi J; Deng Z; Sun G; Qian S; Liu L; Xu B
Eur J Radiol; 2022 Sep; 154():110443. PubMed ID: 35901600
[TBL] [Abstract][Full Text] [Related]
7. STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.
Zhu L; Han Y; Xi X; Fu H; Tan S; Liu M; Yang S; Liu C; Li L; Yan B
Med Phys; 2023 Jul; 50(7):4443-4458. PubMed ID: 36708286
[TBL] [Abstract][Full Text] [Related]
8. Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability.
Zhong F; He K; Ji M; Chen J; Gao T; Li S; Zhang J; Li C
Sci Rep; 2024 Apr; 14(1):9127. PubMed ID: 38644396
[TBL] [Abstract][Full Text] [Related]
9. Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer.
Song B; Kc DR; Yang RY; Li S; Zhang C; Liang R
Cancers (Basel); 2024 Feb; 16(5):. PubMed ID: 38473348
[TBL] [Abstract][Full Text] [Related]
10. Swin-HSTPS: Research on Target Detection Algorithms for Multi-Source High-Resolution Remote Sensing Images.
Fang K; Ouyang J; Hu B
Sensors (Basel); 2021 Dec; 21(23):. PubMed ID: 34884117
[TBL] [Abstract][Full Text] [Related]
11. Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation.
Wu J; Xu Q; Shen Y; Chen W; Xu K; Qi XR
J Clin Med; 2022 Aug; 11(15):. PubMed ID: 35956236
[No Abstract] [Full Text] [Related]
12. SwinOCSR: end-to-end optical chemical structure recognition using a Swin Transformer.
Xu Z; Li J; Yang Z; Li S; Li H
J Cheminform; 2022 Jul; 14(1):41. PubMed ID: 35778754
[TBL] [Abstract][Full Text] [Related]
13. Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs.
Zhou X; Yu G; Yin Q; Yang J; Sun J; Lv S; Shi Q
Diagnostics (Basel); 2023 Feb; 13(4):. PubMed ID: 36832177
[TBL] [Abstract][Full Text] [Related]
14. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.
Ma X; Xia L; Chen J; Wan W; Zhou W
Eur Radiol; 2023 Mar; 33(3):1949-1962. PubMed ID: 36169691
[TBL] [Abstract][Full Text] [Related]
15. S-Swin Transformer: simplified Swin Transformer model for offline handwritten Chinese character recognition.
Dan Y; Zhu Z; Jin W; Li Z
PeerJ Comput Sci; 2022; 8():e1093. PubMed ID: 36262120
[TBL] [Abstract][Full Text] [Related]
16. Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.
Ding H; Feng Y; Huang X; Xu J; Zhang T; Liang Y; Wang H; Chen B; Mao Q; Xia W; Huang X; Xu L; Dong G; Jiang F
Histopathology; 2023 Aug; 83(2):211-228. PubMed ID: 37071058
[TBL] [Abstract][Full Text] [Related]
17. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study.
Yang H; Chen L; Cheng Z; Yang M; Wang J; Lin C; Wang Y; Huang L; Chen Y; Peng S; Ke Z; Li W
BMC Med; 2021 Mar; 19(1):80. PubMed ID: 33775248
[TBL] [Abstract][Full Text] [Related]
18. A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss.
Nie Y; Sommella P; Carratù M; O'Nils M; Lundgren J
Diagnostics (Basel); 2022 Dec; 13(1):. PubMed ID: 36611363
[TBL] [Abstract][Full Text] [Related]
19. A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification.
Üzen H; Fırat H
Health Inf Sci Syst; 2024 Dec; 12(1):33. PubMed ID: 38685986
[TBL] [Abstract][Full Text] [Related]
20. Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.
Su Y; Xia X; Sun R; Yuan J; Hua Q; Han B; Gong J; Nie S
J Imaging Inform Med; 2024 Jun; ():. PubMed ID: 38861071
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
